A variety of new and advanced techniques have emerged in industrial process control, machine control, system surveillance, and condition based monitoring to address drawbacks of traditional sensor-threshold-based control and alarms. The traditional techniques did little more than provide responses to gross changes in individual metrics of a process or machine, often failing to provide adequate warning to prevent unexpected shutdowns, equipment damage, loss of product quality or catastrophic safety hazards.
According to one branch of the new techniques, empirical models of the monitored process or machine are used in failure detection and control. Such models effectively leverage an aggregate view of surveillance sensor data to achieve much earlier incipient failure detection and finer process control. By modeling the many sensors on a process or machine simultaneously and in view of one another, the surveillance system can provide more information about how each sensor (and its measured parameter) ought to be behaving. An example of such an empirical surveillance system is described in U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are incorporated herein by reference. Therein is described an empirical model using a similarity operator against a reference library of known states of the monitored process, and an estimation engine for generating estimates of current process states based on the similarity operation, coupled with a sensitive statistical hypothesis test to determine if the current process state is a normal or abnormal state. Other empirical model-based monitoring systems known in the art employ neural networks to model the process or machine being monitored.
Such empirical model-based monitoring systems require as part of installation and implementation some baseline data characterizing the normal operation of the process or machine under surveillance. The empirical model embodies this baseline normal operational data, and is only as good as the data represents normal operation. A big challenge to the success of the empirical model in the monitoring system, therefore, is to provide sufficiently representative data when building the empirical model. In practice, this is possibly the greatest hurdle for successful implementation of empirical model-based surveillance systems.
A first problem is whether to use data from merely a like process or the identical process with the one being monitored. This is especially significant when monitoring a commodity machine, that is, a machine that will be mass-produced with on-board condition monitoring. Under such circumstances, it may not be possible or practical to gather normal operational data from each machine to build unique empirical models beforehand. What is needed is a way of building a general model into the newly minted machines, and allowing the model to adapt to the unique tolerances and behavior of each particular machine in the field.
A second problem presents itself as the monitored process or machine settles with age, drifting from the original normal baseline, but still being in good operational condition. It is extremely difficult to capture such eventually normal operational data from a process or machine for which that would currently not constitute normal operation. What is then needed is a way for the empirical model to adapt to acceptable changes in the normal operation of the process or machine with age, without sacrificing the monitoring sensitivity that necessitated the empirical model approach in the first place.
A third problem exists where it is not possible to capture the full normal operational range of sensor data from the process due to the financial or productive value of not disrupting the process. For example, in retrofitting an existing industrial process with empirical model-based monitoring, it may not be economically feasible to effectively take the process off-line and run it through its many operational modes. And it may be months or years before all the operational modes are employed. Therefore, what is needed is a way to adapt the empirical model as the operational modes of the process or machine are encountered for the first time.
In summary, in order for an empirical model based process surveillance system to function reliably, the data used to generate the model should span the full process operating range. In many cases that data are not available initially. Therefore, model adaptation is needed to keep the model up-to-date and valid. But adaptation imposes significant hurdles of its own. One such hurdle is determining exactly when to start adapting the model, especially for dynamic non-linear processes. While in some cases human intervention can be relied upon to manually indicate when to adapt, in the vast majority of circumstances it is desirable to automate this determination. Another such hurdle is determining when to stop adapting the model and reinitiate process or machine surveillance. Yet another problem is to distinguish the need for adaptation from a process upset or a sensor failure that should be properly alarmed on. It is highly desirable to avoid “bootstrapping” on a slow drift fault in the process, for example. Yet another problem is to avoid adapting during a period of transition between one stable state and another, during which sensor data may not be typically representative of either any old state or a new state of normal operation of the process or machine. Yet another problem in adapting the empirical model is that the model may grow and become less accurate or less specific due to the addition of new states. Therefore, it would be beneficial to have a way of removing least commonly encountered states from the model while adding the newly adapted states.